Retrieval of Non-rigid (textured) Shapes Using Low Quality 3D Models

Abstract

This paper reports the results of the SHREC 2015 track on retrieval of non-rigid (textured) shapes from low quality 3D models. This track has been organized to test the ability of the algorithms recently proposed by researchers for the retrieval of articulated and textured shapes to deal with real-world deformations and acquisition noise. For this reason we acquired with low cost devices models of plush toys lying on different sides on a platform, with articulated deformations and with different illumination conditions. We obtained in this way three novel and challenging datasets that have been used to organize a contest where the proposed task was the retrieval of istances of the same toy within acquired shapes collections, given a query model. The differences in datasets and tasks were related to the fact that one dataset was built without applying texture to shapes, and the others had texture applied to vertices with two different methods. We evaluated the retrieval results of the proposed techniques using standard evaluation measures: Precision-Recall curve; E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First- Tier (Tier1) and Second-Tier (Tier2), Mean Average Precision. Robustness of methods against texture and shape deformation has also been separately evaluated.